Customer Journey Trace

Stop Viewing Fragmented Interactions. Start Seeing the Complete Customer Story.

In today's complex contact center environment, customer interactions are scattered across numerous platforms—from Amazon Connect and Twilio to PolyAI and Genesys. This fragmentation makes troubleshooting difficult and obscures the true customer experience.

Customer Journey Trace® solves this by providing a single, coherent view of every interaction, handoff, and platform change. We automatically unify all data points into a continuous, end-to-end timeline, giving your teams the clarity they need to excel.

The Power of Unified Clarity

For IT Operations

Faster Issue Resolution: Instantly pinpoint where transfers failed, AI agents handed off inefficiently, or calls dropped across multiple systems.

Troubleshoot with Precision: Visualize connectivity and handoff issues between platforms like never before.

For CX & Service Managers

Complete Visibility: Gain the unified customer view required to understand customer movement across your entire technology stack.

Identify Bottlenecks: Easily detect inefficient multi-platform journeys and critical opportunities for service improvement.

For Quality Assurance Teams

End-to-End Review: See complete customer interactions from initial touchpoint to final resolution for training and improvement initiatives.

Pattern Recognition: Identify recurring issues and training opportunities across your technology ecosystem.

Key Features and Benefits

How It Works

Journeys and Spans

Customer Journey Trace organizes interactions using two key concepts:

Journey: A single customer's complete experience from start to finish, even if it spans multiple platforms. Each journey has a unique Journey ID that links all related interactions.

Span: A segment of the journey representing a specific phase or platform interaction. Common span types include:

• Telephony - Voice call segments
• IVR - Interactive Voice Response menu navigation
• AI Conversation - Interactions with chatbots or voice assistants
• Agent Interaction - Time spent with human agents
• Queue - Wait time in queue
• Platform Handoff - Transfers between systems

Each span contains detailed information about what happened during that phase, including timestamps, metrics, attributes, and any issues detected.

Supported Platforms

Customer Journey Trace works with 50+ CX platforms, including: Amazon Connect, Genesys Cloud, NICE CXone, Twilio, PolyAI, BlandAI, LiveKit, VAPI, Pipecat and more.

For a full list of supported AI Customer Service, CCaaS, Voice AI and CX platforms visit: Operata Integrations Hub

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Platform Integration

Verified Platforms

Platforms listed as Verified in the Operata Integrations Hub are ready for immediate data collection.

Other Platforms (Not yet Verified)

Platform integrations that are yet to be Verified may require quality review by an Operata CX specialist before traces are fully visible. This ensures data accuracy and proper correlation across systems.

Request Verification

Customers: To request Verification for a CX Platform you are using, visit: https://operata.com/customer-integration-verification

Software Vendors: To request Verification for your AI Customer Service, CCaaS, Voice AI or CX Product, visit: https://operata.com/isv-integration-verification

Automatic Enablement

Customer Journey Trace is automatically enabled for all Operata customers. No special configuration, licensing, or permissions are required to start using it.

Accessing Journey Explorer

Journey Explorer is your portal to Customer Journey Trace:

1. Open the Operata platform
2. Click Journey Explorer in the left-hand vertical menu
3. Browse or search for customer journeys

All user roles (Viewer, User, and Admin) have access to Journey Explorer by default.

Understanding the Journey Visualization

Flame Chart Timeline

Journey Trace uses a flame-chart visualization to display customer journeys:

• Horizontal axis - Time progression from left to right
• Vertical layers - Different services and platforms
• Colored bars - Individual spans representing phases of the journey
• Bar length - Duration of each phase
• Bar position - When the phase occurred in the journey timeline

Spans are color-coded and labeled by type:

• Telephony spans
• IVR interaction spans
• AI agent conversation spans
• Agent interaction spans
• Platform handoff spans

Drilling Into Details

Click on any span to view:

• Attributes - Platform IDs, phone numbers, agent names, queue names, and other metadata
• Metrics - Duration, wait times, audio quality metrics, and performance data
• Logs - Detailed event logs from the platform
• Insights - Automatically detected issues or notable events (when applicable)

AI-Generated Summaries

Each journey includes an AI-generated summary that provides context about:

• The customer's intent or reason for contact
• Key events that occurred during the journey
• Platforms involved and handoffs that occurred
• Overall outcome and resolution status

This helps teams quickly understand complex journeys without manually reviewing every span.

Common Use Cases

Investigating Dropped Calls and Transfer Issues

When customers report dropped calls or failed transfers, Journey Trace helps you:

1. Identify the exact moment the issue occurred by examining the timeline
2. See which platform was handling the interaction when the problem happened
3. Review handoff details to understand if a transfer between platforms failed
4. Check metrics like audio quality, network conditions, and error logs
5. Correlate with insights to see if known issues (packet loss, timeouts) were detected

Example Scenario: A customer calls a PolyAI voice agent, gets transferred to Amazon Connect, but the call drops during transfer. Journey Trace shows:

• The PolyAI span ending normally
• A platform handoff span showing the transfer initiation
• Gaps or errors in the Amazon Connect span
• Network or telephony insights flagged during the handoff

This pinpoints whether the issue was with PolyAI's transfer logic, Amazon Connect's ability to receive the call, or network connectivity between systems.

Analyzing AI Agent Handoffs

Understanding when and why AI agents hand off to human agents is critical for optimizing your CX automation:

1. View the AI conversation span to see what the customer asked and how the AI responded
2. Check handoff triggers - Did the AI determine it couldn't help? Did the customer request a human? Did the conversation reach a timeout?
3. Measure handoff timing - How long did the AI interaction last before handoff?
4. Evaluate queue experience - How long did the customer wait after the AI decided to transfer?
5. Review agent interaction - Did the agent have proper context from the AI conversation?

Example Scenario: You notice that PolyAI is handing off to human agents more frequently than expected. Journey Trace reveals:

• AI conversation spans show specific intents the AI struggles with
• Handoff spans indicate customers are requesting agents early in the conversation
• Queue spans show increased wait times due to higher human agent demand
• Agent interaction spans show agents asking for information the AI already collected

This insight can guide improvements to your AI agent's capabilities, handoff logic, or information passing between systems.

Tips for Using Journey Explorer

Search and Filter

• Search by phone number - Find all journeys for a specific customer
• Filter by date range - Focus on journeys during specific time periods
• Filter by platform - See journeys involving specific services
• Filter by insight - Find journeys with specific issues detected

Understanding Journey Legs

Journeys are divided into "legs" representing each platform involved:

• Leg 1 - First platform (where the journey started)
• Leg 2 - Second platform (after first handoff)
• Leg 3+ - Additional platforms as customer is transferred

The journey leg counter helps you understand how many times a customer was moved between systems.

Interpreting Timeline Gaps

Gaps in the timeline can indicate:

• Expected transitions - Normal handoff time between platforms
• Data delays - Some platforms (like Amazon Connect CTRs) may report data 5-20 minutes after interactions end
• Missing data - Incomplete platform integrations or data collection issues
• Customer hold time - Periods where the customer was on hold or in queue

Check span details and logs to understand what occurred during gaps.

Platform-Specific IDs

Each span includes the original platform's interaction ID:

• Amazon Connect: ContactId
• Twilio: CallSid
• PolyAI: Call ID
• Genesys: Conversation ID

Use these IDs to cross-reference with platform-native tools for additional details not available in Journey Trace.

Understanding Data Timing

Real-Time vs. Delayed Data

Different platforms report data at different speeds:

• Real-time data - Available within seconds (agent events, real-time streams)
• Near real-time - Available within minutes (most telephony events)
• Delayed data - Available after 5-20+ minutes (Amazon Connect CTRs, batch processing)

Journey Trace assembles the complete picture as data arrives from each platform. Early views of a journey may be incomplete if delayed data hasn't arrived yet.

Journey Assembly

Customer Journey Trace uses automatic correlation to link interactions across platforms:

1. Journey ID - A unique identifier generated for each customer journey
2. Platform IDs - Each platform's native interaction ID is preserved
3. Correlation points - Transfer events, SIP headers, API tokens, and CTR attributes link platforms together
4. Asynchronous assembly - Journeys are built progressively as data arrives from each system

This means you can start viewing a journey while it's in progress, and additional spans will appear as data becomes available.

Troubleshooting

Journey Not Appearing

If you can't find a journey you're looking for:

1. Check the date range - Expand your search window
2. Verify platform integration - Ensure the platform is sending data to Operata
3. Wait for delayed data - Amazon Connect CTRs can arrive 20+ minutes late
4. Check customer identifier - Search by phone number or other known identifiers

Missing Spans

If a journey is missing expected spans:

1. Data may still be arriving - Refresh after a few minutes
2. Platform integration issue - Some platforms may require setup or QC review
3. Correlation pending - The system may still be linking late-arriving data
4. Platform not reporting - Verify the platform is configured and operational

Incomplete Journey Details

If span details are missing information:

1. Platform limitations - Not all platforms report all data types
2. Privacy controls - Some data may be filtered based on your privacy settings
3. Integration scope - The platform integration may not include all telemetry

Elevate Your CX Operations

Customer Journey Trace delivers what modern contact centers need: complete visibility across fragmented systems. Elevate your Quality Assurance, accelerate issue resolution, and gain unprecedented insight into your customer experience.

Stop piecing together data from multiple platforms. Start seeing the complete customer story with Customer Journey Trace.

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Ready to explore? Click Journey Explorer in the left menu to start viewing customer journeys across your CX platforms.